Remotely Controlled Electrochemical Degradation of Metallic Implants.
Boris RivkinFarzin AkbarMartin OttoLukas BeyerBirgit PaulKonrad KosibaTobias GustmannJulia Kristin HufenbachMariana Medina-SánchezPublished in: Small (Weinheim an der Bergstrasse, Germany) (2024)
Biodegradable medical implants promise to benefit patients by eliminating risks and discomfort associated with permanent implantation or surgical removal. The time until full resorption is largely determined by the implant's material composition, geometric design, and surface properties. Implants with a fixed residence time, however, cannot account for the needs of individual patients, thereby imposing limits on personalization. Here, an active Fe-based implant system is reported whose biodegradation is controlled remotely and in situ. This is achieved by incorporating a galvanic cell within the implant. An external and wireless signal is used to activate the on-board electronic circuit that controls the corrosion current between the implant body and an integrated counter electrode. This configuration leads to the accelerated degradation of the implant and allows to harvest electrochemical energy that is naturally released by corrosion. In this study, the electrochemical properties of the Fe-30Mn-1C/Pt galvanic cell model system is first investigated and high-resolution X-ray microcomputed tomography is used to evaluate the galvanic degradation of stent structures. Subsequently, a centimeter-sized active implant prototype is assembled with conventional electronic components and the remotely controlled corrosion is tested in vitro. Furthermore, strategies toward the miniaturization and full biodegradability of this system are presented.
Keyphrases
- soft tissue
- high resolution
- end stage renal disease
- ejection fraction
- newly diagnosed
- gold nanoparticles
- prognostic factors
- single cell
- ionic liquid
- healthcare
- cell therapy
- stem cells
- computed tomography
- mass spectrometry
- drug delivery
- patient reported outcomes
- label free
- deep learning
- single molecule
- low cost
- atomic force microscopy